• Energy Research

Battery energy storage systems (BESSs) are often used for demand charge reduction through monthly peak shaving. However, during economic analysis in the feasibility stage, BESSs are often sized, and BESS revenue is quantified based on 1 h load and/or solar output data for one year. To quantify the error in the demand charge from coarse-resolution modeling, the effect of two temporal resolutions, 15 min and 1 h, on peak load reduction is compared across a battery rating space defined by the power capacity and energy capacity. A linear program of the system optimizes the peak of the net load and the associated demand charge assuming perfect forecasts. Based on the 15 min load profile of a particular day, a critical power (CP) and critical energy (CE) can be defined, yielding a critical point in the power-energy space. Based on the difference of demand charge (DoDC) across the two load profiles at different temporal resolutions for a real building, the battery rating space is divided into three different regions: oversized region, power-constrained region, and energy-constrained region, which are separated by CP and CE. The DoDC in the power-constrained and energy-constrained regions is explained by time averaging effects and the load sequence at high resolutions. In the power-constrained region of the battery rating space, the difference between the original 15 min peak and the 1 h average peak persists in the optimized net load until the battery power capacity is sufficiently large. In the energy-constrained region, averaging may change the peak period duration, which depends on the sub-hourly sequence of the original load data. Through artificial load data and reordering of real load data, we demonstrate that the sequence effect causes energy-constrained batteries to underestimate peak shaving and demand charge reduction. Demand charge savings were especially sensitive to the BESS power capacity: for a ≈50 kW load, demand charge errors were up to $53 for power-constrained batteries and were an order of magnitude smaller for energy constrained batteries. The power capacity of the battery should be carefully considered when interpreting results from optimizations at low resolutions.

Battery energy storage systems (BESSs) are often used for demand charge reduction through monthly peak shaving. However, during economic analysis in the feasibility stage, BESSs are often sized, and BESS revenue is quantified based on 1 h load and/or solar output data for one year. To quantify the error in the demand charge from coarse-resolution modeling, the effect of two temporal resolutions, 15 min and 1 h, on peak load reduction is compared across a battery rating space defined by the power capacity and energy capacity. A linear program of the system optimizes the peak of the net load and the associated demand charge assuming perfect forecasts. Based on the 15 min load profile of a particular day, a critical power (CP) and critical energy (CE) can be defined, yielding a critical point in the power-energy space. Based on the difference of demand charge (DoDC) across the two load profiles at different temporal resolutions for a real building, the battery rating space is divided into three different regions: oversized region, power-constrained region, and energy-constrained region, which are separated by CP and CE. The DoDC in the power-constrained and energy-constrained regions is explained by time averaging effects and the load sequence at high resolutions. In the power-constrained region of the battery rating space, the difference between the original 15 min peak and the 1 h average peak persists in the optimized net load until the battery power capacity is sufficiently large. In the energy-constrained region, averaging may change the peak period duration, which depends on the sub-hourly sequence of the original load data. Through artificial load data and reordering of real load data, we demonstrate that the sequence effect causes energy-constrained batteries to underestimate peak shaving and demand charge reduction. Demand charge savings were especially sensitive to the BESS power capacity: for a ≈50 kW load, demand charge errors were up to $53 for power-constrained batteries and were an order of magnitude smaller for energy constrained batteries. The power capacity of the battery should be carefully considered when interpreting results from optimizations at low resolutions.

Abstract 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.

Abstract 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.

Abstract Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

Abstract Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

Abstract Satellite derived global horizontal solar irradiance (GHI) from the SUNY modeled dataset in the National Solar Radiation Database (NSRDB) was compared to measurements from 27 weather stations in California during the years 1998–2005. The statistics of spatial and temporal differences between the two datasets were analyzed and related to meteorological phenomena. Overall mean bias errors (MBE) of the NSRDB–SUNY indicated a GHI overprediction of 5%, which is smaller than the sensor accuracy of ground stations. However, at coastal sites, year-round systematic positive MBEs in the NSRDB–SUNY data up to 18% were observed and monthly MBEs increased up to 54% in the summer months during the morning. These differences were explained by a tendency for the NSRDB–SUNY model to overestimate GHI under cloudy conditions at the coast during summer mornings. A persistent positive evening MBE which was independent of site location and cloudiness occurred at all stations and was explained by an error in the time-shifting method applied in the NSRDB–SUNY. A correction method was derived for these two errors to improve the accuracy of the NSRDB–SUNY data in California.

Abstract Satellite derived global horizontal solar irradiance (GHI) from the SUNY modeled dataset in the National Solar Radiation Database (NSRDB) was compared to measurements from 27 weather stations in California during the years 1998–2005. The statistics of spatial and temporal differences between the two datasets were analyzed and related to meteorological phenomena. Overall mean bias errors (MBE) of the NSRDB–SUNY indicated a GHI overprediction of 5%, which is smaller than the sensor accuracy of ground stations. However, at coastal sites, year-round systematic positive MBEs in the NSRDB–SUNY data up to 18% were observed and monthly MBEs increased up to 54% in the summer months during the morning. These differences were explained by a tendency for the NSRDB–SUNY model to overestimate GHI under cloudy conditions at the coast during summer mornings. A persistent positive evening MBE which was independent of site location and cloudiness occurred at all stations and was explained by an error in the time-shifting method applied in the NSRDB–SUNY. A correction method was derived for these two errors to improve the accuracy of the NSRDB–SUNY data in California.