Load Duration Curve Research Articles

A district-level building electricity use profile simulation model based on probability distribution inferences

District-level building energy systems play a significant role in urban energy networks in the future. Understanding the key distributive features of district electricity use profiles is essential for the optimal planning and design of energy networks. Due to the diversity of building electricity use characteristics, the district-level electricity use profile exhibits a prominent “peak staggering effect.” Current physics-based and statistical models cannot fully represent realistic distributions and the uncertainties of district profiles. Thus, it is critical to quantitatively investigate the changing patterns and distributive features of electricity use profiles at various district levels. This paper proposes a novel approach for district building electricity use profile simulation. Probability distribution inference methods integrating Gaussian Mixture Model (GMM)/lognorm distribution fitting, singular value decomposition (SVD)-based feature transformation, and distribution addition theorems have been proposed to generate the feature parameters of electricity use profiles at various district scales, thus generating simulated district electricity use profiles. The performance of the proposed model was validated using engineering-informed metrics, including peak demands, load duration curves, and standard deviations of the load parameters. The results of the case study suggest that the average relative error of the 99 % peak demand is reduced from 17.60 % in the baseline model to 3.48 % in the proposed model, the average relative error of the duration of 2Qm reduced from 40.82 % in the baseline model to 0.99 % in the proposed model, and the average relative error of the standard deviation of load parameters was reduced from >100 % in the baseline model to <35 % in the proposed model. The results indicate a better quantification of district electricity use distributions and uncertainties, providing practical tools to support the capacity design and optimization of integrated district energy systems.

A semi-automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves.

Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi-automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time. An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three-phase system, using the average current measured across all three phases, a 480V reference voltage for both machines, and vendor-provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC'), smoothed by convolution with a window function (sLDC'), and used to detect transitions between different system modes including (in descending power levels): scan, prepared-to-scan, idle, low-power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1-month LDC, for each nonproductive system mode (i.e., prepared-to-scan, idle, lower-power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data). The validation revealed differences in mean power values<1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2kW and the total energy consumed for 1 month ranged from 11106 to 14466kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends. A semi-automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. The methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners.

Do peak energy loads and peak GHG emissions correlate in buildings? Insights from emission duration curves and emission event duration curves analysis

This research proposes and applies emission duration curves (EDCs) and emission event duration curves (EEDCs) in a novel way for comprehensive analysis of greenhouse gas (GHG) emissions in correlation with building energy consumption. Examining a 2018 high-rise building in Ottawa, we found a moderate Pearson correlation of 0.3 between hourly energy and GHG emissions, despite a significant p-value. This indicates that peak energy loads and emissions peaks do not necessarily align, underscoring the need for a new operational strategy. The EDC and Load Duration Curve (LDC) patterns further accentuated these differences, suggesting that electrical emissions are more concentrated in specific periods compared to electrical loads. For our case study, 12.04% of yearly GHG emissions occurred within just 1% of the year, contrasting with only 2.14% of the annual energy use. The study also highlighted the potential of a demand response event through a simulated voluntary outage during peak emission intervals as a viable GHG mitigation strategy. Collectively, these insights stress the importance of a comprehensive perspective on building operations, their interactions with the grid, and the necessity for tailored sustainable building management strategies moving forward.

Long-term equilibrium in electricity markets with renewables and energy storage only

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.

Development of Daily Flow Expansion Regression and Web GIS-Based Pollutant Load Evaluation System

This study accounted for the importance of daily expansion flow data in compensating for insufficient flow data in a watershed. In particular, the 8-day interval flow measurement data (intermittent monitoring data) could cause uncertainty in the high- or low-flow conditions that have been used to estimate the flow duration curve (FDC) and the load duration curve (LDC) used in Total Maximum Daily Load (TMDL) evaluation in Korea. Thus, this study developed a method to expand the 8-day interval flow data (missing data) to daily flow data in order to evaluate the Total Maximum Daily Load (TMDL) appropriately in a watershed. We employed the machine learning technique (the gradient descent method provided by the Google TensorFlow package) to develop a regression for expanding the 8-day interval flow data. The method was applied in the Nakdong River basin located in Korea to collect the 8-day interval and daily flow data from a number of gauging stations. The results of the expanded daily flow were evaluated through the RMSE, MAE, IOA, and NSE, and the valid expanded daily flow data were obtained for the 29 TMDL gauging stations (IOA 0.84~0.99, NSE −0.18~0.99). A good performance in the creation of daily flow data (continuous data) from the 8-day interval flow data (intermittent data) was shown using the proposed method. In addition, the Web GIS-based pollutant load assessment system was developed to evaluate the TMDL; it included the daily data expansion method and provided the pollution load characteristics objectively and intuitively. This system will help decision makers, such as environmental regulators, researchers, and the general public, and support their decision making for pollution source management with accessible and efficient tools for understanding and addressing water quality issues.

The Impact of Non-Point Source (NPS) Management on Non-Point Source Reduction and Water Cycle Improvement in an Urban Area

Suwon, the capital and largest city of Gyeonggi-do, South Korea, was designated as a non-point source management area in 2010. The management period ended in 2020, so follow-up measures are needed. In this study, we investigated several projects implemented in Suwon for urban water cycle improvement and non-point source pollution reduction, and the long-term management effects were analyzed to suggest policy directions such as the revision of designation notices. During the 10-year management period in Suwon, the population and lot area continued to increase, and the non-point source-based annual Biochemical Oxygen Demand (BOD) discharge loads also increased by approximately 25% at the half sub-basins Hwangguji-cheon and Woncheonri-cheon in 2020 compared to 2010. Even under these conditions, statistical analyses show that the Biochemical Oxygen Demand (BOD) and Total Phosphorus (TP) concentrations monitored at the outlet of basin were decreased due to the promotion of a large-scale sewer management project as well as non-point source pollution reduction projects. Also, the field monitoring data-based Load Duration Curve (LDC) analysis results indicate that the loads decreased in the high-flow period of 2020 compared to 2015. Also, the Normalized Difference Vegetation Index (NDVI) values calculated using satellite images since 2017 tended to increase slightly during the period when the impervious area estimated using the land registration map increased. It is assumed that using the current calculation method for impervious areas has limitations regarding its ability to reflect changes in the small-scale Low-Impact Development (LID) facility and in ecological/landscape areas. On the other hand, the annual variation in direct runoff estimated at the outlet using three hydrograph separation methods did not show any improvement with regard to storm water retention during the management period. These results reveal that the effects on urban water cycle improvement, such as peak flow reduction and base flow increase, may not be noticeable despite some progress in reducing non-point source pollution and increasing green area. Therefore, additional efforts directed towards non-point source pollution management focused on water cycle improvement are required in the city, especially in the sub-basins with higher pollution loads such as Hwangguji-cheon and Seoho-cheon.

The Residual Load Duration Curve (rLDC) to model an energy system

This paper develops a framework to represent the action of different generation and balancing technologies based on the statistics of the demand and generation using the concept of the Load Duration Curve (LDC). The aim is to capture the statistics in terms of energy and power provision from a generation type given by its inherent resource, technical and economic constraints. This paper provides a brief overview of the fundamental types of generation and balancing, and how their action modifies the load duration curve to eventually arrive at a balanced system. Based on empirical data from the UK National Grid, the statistical properties of renewable energy sources and of the energy and power capacities of energy storage technologies, different basic functional relationships between the residual Load Duration Curves (rLDC) will be derived for these technologies.

The effect of energy storage on the Residual Load Duration Curve (rLDC) of a system with high Renewable Energy contribution

This paper evaluates how energy storage acting in a system with a very high contribution from variable Renewable source can reduce peak loads. The methodology uses observed demand and renewable generation data to construct scenarios of increasing renewable contribution complemented by energy storage. The analysis is based on the concept of creating load duration curves for the period over which a set of storage technologies can act to reduce peak loads by storing energy during low loads. The models used here are developed on the premise that different types of storage are deployed for different purposes. For the scope of this work, three types were used. The first to manage loads within a day, the second acting over a period of a week, and the third at a much longer, seasonal scale

A novel hybrid centralised decentralised framework for electric vehicles coordination

AbstractHybrid Centralised‐Decentralised Electric Vehicle (EV) coordination policy in the urgent charging scenario is presented. First, a robust and complex optimisation problem considering several key features affecting EV coordination is formulated. Then, a solution strategy for the formulated problem is proposed by decomposing the formulated problem into an EV coordination and simple optimisation problem. The decentralised rule‐based EV coordination strategy works on the principle of direct load flattening and utilises practical EV aggregator‐customer interaction, customer behaviour, and temporospatial shifting of the EVs to flatten the load duration curve at the charging station. Then, the centralised optimisation problem is solved to minimise the operation cost, decrease the power loss, and decrease congestion in the grid. A comparison between uncoordinated and coordinated charging in the case study conducted on the IEEE 24 bus system shows that the proposed approach reduces the average EV load by 1283.26 kW/min, average power loss by 2.465 kW/min, and operation cost by 61.99 $/min during the peak hours.

A novel physical-feature-based approach for stochastic simulation of typical building electricity use profiles

Electrification in buildings is an inevitable trend under carbon neutrality targets and this increases the penetration rate of renewable energy sources in power grids. Understanding the key characteristics of electricity use profiles is the basis for advanced planning and design of integrated building energy systems. Electricity use profiles of buildings are driven by various external factors, and exhibit significant randomness. Current statistical, clustering and generative studies have not fully revealed the key aspects of building electricity use profiles. This study proposes a novel physical-feature-based building electricity use profile model. Five physical feature parameters were extracted from annual daily and diurnal (sub-)hourly time-scales, including the power-temperature curves, weekend ratio, variation factor, weekday schedule and weekend schedule. The proposed model was validated using two engineering-informed metrics: peak demand and load duration curves. In a case study of 4,168 non-residential buildings, the average relative simulation error of 99% peak demand was reduced from 8.53% in the clustering model to 1.44% in the proposed model, the average p-value for the K-S test of the simulated peak hour distributions increased from 0.00059 in the clustering model to 0.28 in the proposed model, and the average CV-MAE of the load duration curve was reduced from 7.24% in the clustering model to 5.36% in the proposed model. The proposed methodology and results present fundamental references of typical building electricity use profiles, and to provide useful support for advanced building energy system planning and design.

Medium‐term forecasting of daily aggregated peak loads from heat pumps using clustering‐based load duration curves to calculate the annual impact on medium to low voltage transformers

AbstractThe energy transition drives the adoption of heat pumps (HPs). Their peak loads have a large impact on distribution networks. Therefore, the proposed methodology calculates the annual impact on medium to low voltage (MV/LV) transformers based on a medium‐term forecast of daily aggregated peak loads from HPs using the number, their rated power, a normalised load duration curve, and the daily average ambient temperature. The forecast differentiates between daily aggregated peak loads of HPs with different heating demands and the varying impact due to seasonal weather differences. First, 221 measured residential HPs over two years were randomly selected and clustered to identify different heating demands. These clusters were used to calculate four representation functions. Second, 108 other measured HPs were clustered and used to forecast the aggregated peak load with the calculated four representation functions. These forecasts were used to calculate the annual impact on an MV/LV transformer with a daily time resolution. These results indicate the periods over a year that the MV/LV transformer is at risk of congestion and it indicates the potential to mitigate congestion through demand‐side management.

Hybrid model for daily streamflow and phosphorus load prediction.

Environmental factors, such as climate change and land use changes, affect water quality drastically. To consider these, various predictive models, both process-based and data-driven, have been used. However, each model has distinct limitations. In this study, a hybrid model combining the soil and water assessment tool and the reverse time attention mechanism (SWAT-RETAIN) was proposed for predicting daily streamflow and total phosphorus (TP) load of a watershed. SWAT-RETAIN was applied to Hwangryong River, South Korea. The hybrid model uses the SWAT output as input data for the RETAIN. Spatial, meteorological, and hydrological data were collected to develop the SWAT to generate high temporal resolution data. RETAIN facilitated effective simultaneous prediction. The SWAT-RETAIN exhibited high accuracy in predicting streamflow (Nash-Sutcliffe efficiency (NSE): 0.45, root mean square error (RMSE): 27.74, percent bias (PBIAS): 22.63 for test sets), and TP load (NSE: 0.50, RMSE: 423.93, PBIAS: 22.09 for test sets). This result was evident in the performance evaluation using flow duration and load duration curves. The SWAT-RETAIN provides enhanced temporal resolution and performance, enabling the simultaneous prediction of multiple variables. It can be applied to predict various water quality variables in larger watersheds.

Power System Reliability Evaluation Based on Chronological Booth–Baleriaux Method

The complexity of modern power systems is increasing because of the development of various intermittent generators. In practical reliability evaluations, it is essential to include both the failure of conventional generators and the output characteristics of renewable energy; the use of the latter has increased rapidly. The weather-dependent nature of renewable energy output, which is inexplicable in the load duration curve method, highlights the need for further study of the methods of a reliability evaluation that can consider temporal characteristics. This paper proposes a deterministic reliability evaluation method based on the Booth–Baleriaux method, chronologically extended to address the preventative maintenance schedule of a generator and the characteristics of renewable energy. The proposed method was applied to an IEEE reliability test system for performance verification, and a reliability evaluation was performed considering various chronological patterns. The proposed method was also applied to determine the adequate capacity reserve that should be installed in a Korean power system. The proposed method is stable, and it produced robust results.

Uncertainty and sensitivity analysis of generation expansion planning in hybrid nuclear and renewable energy system

In this study, generation expansion planning (GEP) is executed with the lowest cost and pollutant emission, highest efficiency, and lowest loss of load probability (LOLP). A hybrid energy system integrated with nuclear and renewable sources will be a potential candidate for a carbon-free energy network. Integration of nuclear and renewable energy tackles the demerits present when they operate individually. In this new approach, the expansion of modular reactors and renewable energy is discussed in the presence of uncertainties raised by load demand, load duration curve (LCD), energy carrier prices, power plant depreciation rates, weather conditions, and renewables output. Moreover, sensitivity analysis is fulfilled to find the best reactor expansion capacity. Firstly, all uncertainties are modeled by their probability density function (PDF). Then a modified Metropolis Markov chain Monte Carlo (MCMC) coupled with the Wilks method was implemented to predict the stochastic behavior of different uncertainty sources exactly so that reaching to 90% reliability of the final plan. Finally, the approach is integrated into WASP-IV software to provide the optimal number of sources with optimal cost and pollutants. The results indicate that the LOLP index in the last year of the study period is only 1.4%–2.5%, and the number of installed nuclear units are 3 SMART reactor per 2 years just by 10% of tolerance, owing to considering all the uncertainties in the expansion process after sensitivity analysis. Furthermore, the emission of pollutants is about zero.

A new approach based on off-design R-curve and thermal load duration curve for the design and optimization of bioresources driven cogeneration Rankine cycle: Application on industrial site in Nova Scotia, Canada

Decarbonization objectives in industrial processes impose major changes for industrial energy supply. The combination of decarbonization of heat and electric load can be achieved by cogeneration plant driven by clean energy resources. Despite of new innovative cogeneration cycle being investigated, Rankine cycle cogeneration plant driven by biomass or biofuels stills the most attractive solution in low solar irradiance countries. The aim of this study is to establish a new original method combining a steady state and dynamic approach to solve and link optimal conceptual design problem and operation strategies of the plant. This approach is applied also to evaluate the worthiness of adopting a thermal energy storage solution. Breakdown of electricity and thermal cost of cogeneration plant is established for the first time for the sake of mapping energy prices of different decarbonization solutions. A thermodynamic and economic model to predict the performances of the cogeneration plant at design and off-design conditions was developed.Three off-design operation strategies are identified: the first engenders steam throttling, the second induces part-load operation of the condensing turbine, and the third requires an auxiliary boiler to meet peak thermal demand. A new R-curve named off-design R-curve is developed to predict cycle performances during these operations, and then combined with the thermal load duration curve to predict energy consumption. Subsequently, an optimization procedure combining the R-curve tool and cost break-down of electricity and thermal energy is carried out. The energy prices and the annualized capital cost R-curves are summed up to optimize the design of power to heat ratio.A case study in an industrial site in Nova Scotia (Canada) is considered. The method proves its simplicity and effectiveness in the conceptual design of the plant, as well as predicting different costs of energy and the optimization of the R ratio. Case study results show that biomass energy cost saving rises with power production to achieve its maximum at Rd equal to 1.23, while biofuel energy cost saving decreases with Rd and was achieved only when Rd is lower than 0.7. Combining the annualized cost with the energy cost reveal that the optimal Rd is at 1.07 for biomass and 0.21 for biofuel, respectively. Moreover, with the introduction of the off-design R curve, it was found that the cogeneration efficiency drops down to 18 % (from 0.52 to 0.34 at Rd equal to 1.23) and the power production down to 60 % when the process load is double the average load. Hence, more accurate energy cost-saving values and optimal on-site power production are achieved. Energy cost saving drops by 7 %, which lowers the value of Rd where biofuel cost saving is achieved to 0.3, while the optimal value stays the same for biomass. Besides, the maximum potential of thermal energy storage in energy saving is accounted. The saving in the case study is low at 5–6 %, but generally it is dependent on the frequency at which the thermal load occurs. The method developed proves its effectiveness in solving conceptual design problems, and can used to rigorously evaluate new innovative configurations of cogeneration plants. Challenges and future works to improve this method are also addressed.

Total Maximum Daily Load Application Using Biological Oxygen Demand, Chemical Oxygen Demand, and Ammoniacal Nitrogen: A Case Study for Water Quality Assessment in the Perai River Basin, Malaysia

Water shortage has been an issue for urbanized areas. For the Penang state in Malaysia, it is forecast that there will be a significant increase in water demand in the future. Penang authorities in Malaysia are trying to find an alternative water source to overcome the problem, with one of the options being the Perai River catchment. However, the river water quality was found to be polluted and not suitable to be used for water extraction for domestic consumption. This paper aims to study the pollution level variation due to changes in rainfall during the year in the Perai River Basin, and estimate the TMDL of the river in a particular case for BOD, COD, and NH3N parameters. A water quality model was developed for the Perai River, Jarak River and Kulim River using InfoWorks ICM. The year 2016 was selected as a model event due to data availability. BOD, COD and NH3N concentrations were used for TMDL calculation, and the load duration curve approach was used to estimate TMDL. The tidal effect at the downstream of the Perai River was found to impact the data analysis in the river stretch. It was found that pollutant load exceedance was the highest during the rainy season and the problematic pollutant was NH3N. Thus, local authorities need to focus on tidal and seasonal change factors when developing action plans to manage water quality issues in this basin.

Evaluation of Non-Point Source Pollution Load Characteristics and Identifying Management Sub-Basin Area and Best Management Practices Using HSPF Model in Yongdam Dam Watershed

Objectives : To identify and estimate the non-point source (NPS) pollution loads in the Yongdam watershed, and to take an appropriate NPS management plan suitable for the environmental conditions of the sub-basin of Yongdam watershed.Methods : The NPS pollutant loads of the Yongdam watershed was investigated to establish the hydrological simulation program-fortran (HSPF) model for the study area so that the characteristics of non-point runoff were simulated. Furthermore, the detailed catchment area subject to non-point pollution management was selected and the effect of reducing non-point pollution was examined based on the various simulation results.Results and Discussion : The three sub-basins, including specific component of catchments, of Yongdam watershed was selected according to the load intensity and distribution of NPS pollution. The result of analyzing the load flowing into the downstream by rainfall intensity revealed that the pollutant load accounted for 11.8~19.9% in the dry season, and for Jinan-cheon sub-basin the load was high in the range of 10~30 mm while Janggye-cheon sub-basin and downstream of Janggye-cheon junction sub-basin showed a high load in the range 30~60 mm. In addition, the load duration curve (LDC) showed the excess frequency of BOD load was high when the flow rate increased at the end of stream for each sub-basin, whereas the load excess frequency was high in T-P in the entire flow range. LDC analysis led to us that the water quality of stream was affected by point and non-point pollutants in a complex way. When the ground cover in a farm were installed by selecting 10 detailed catchments with high need for NPS pollution management, the reduction of T-P load was estimated to be reduced by 325.256~995.912 g/d in Jinan-cheon sub-basin, 1279.813~3327.513 g/d in Janggye-cheon sub-basin, and 1316.831~3555.443 g/d in downstream of Janggye-cheon junction sub-basin.Conclusion : Through various simulation experiments using the HSPF model, it was feasible to select an catchment subject to NPS pollution control in detail and evaluate the effect of reducing NPS pollution.

마산만의 수질 개선을 위한 우선순위 하천 선정

This study aimed to select the priority streams requiring improvement in water quality among the 17 streams flowing into the Masan Bay. Several methods were employed to select the priority streams, including calculation of discharge load and discharge load per unit area, analyses of the impervious rate and road ratio using land cover characteristics, grouping of branch streams, and the calculation of load duration curve excess rate. Watershed data, including flow and water quality data, were collected and monitoring was conducted in the Masan Bay watershed from 2005 to 2021 at the endpoints of the streams. 1) Five and six streams were identified as the subjects of priority management, according to the highest load rates by subwatershed and per unit area, respectively. 2) Two streams appeared to be the subjects of priority management, according to land cover characteristics that indicated an impervious ratio >50 % and a road ratio >10 %. 3) Stream grouping was carried out to identify group A water quality streams with high load and flow rates; six and eight streams were found to be the subjects of priority management, respectively. 4) As a result of selecting streams with an average excess flow >50 % using a flow duration curve, six streams were found to be the subjects of priority management. 5) The analysis of the five indicators revealed that Shinicheon and Samhocheon streams require priority management to improve water quality in the Masan Bay area, particularly management of point-source pollution. Continuous management is also required for Cheoksan, Naedong, and Changwon streams, which were identified as the subjects of priority management based on four indicators.

Flow patterns and chemical loads in the middle Olifants River, Limpopo River System, South Africa

Historical data (July 1998 – June 2018) for the middle Olifants River, Limpopo River system, were compiled to evaluate the dynamics of selected water physico-chemical parameters with river discharge. The concentration of most water quality parameters increased over time. However, these concentrations are rapidly decreased, or reset, by the rapid filling, or spilling, of Loskop and/or Flag Boshielo Dams during a high-flow event. The flow-duration curves for stations below impoundments in the middle catchment on the Olifants River are typical of highly regulated rivers, although releases from Flag Boshielo Dam were more consistent through the 20 years. No outflow from Loskop Dam was recorded for 5.4% of the 20 years. The load-duration curves for gauging weirs on the Olifants and Elands rivers immediately upstream of Flag Boshielo Dam showed that the ‘tolerable’ concentrations were exceeded for total dissolved solids at the 60th and 20th percentiles of the flow, respectively. In addition, records for electrical conductivity, sodium, chloride, and alkalinity frequently exceeded the ‘tolerable’ concentrations at these sites. The results for Loskop and Flag Boshielo dams are generally lower than the tolerable concentrations for the parameters evaluated. Management of the flow regulation of the Olifants River should be aimed at meeting the water quality stipulations for all users in the agricultural, domestic, industrial, and recreational sectors.

Hydroelectricity Modeling for Low‐Carbon and No‐Carbon Grids: Empirical Operational Parameters for Optimization and Dispatch Models

AbstractHydroelectric power has unusual technical characteristics that could become more valuable as the penetration of variable generation renewables grows, but its use for electricity generation is constrained by complex physical, safety, and socioenvironmental considerations. Hydroelectricity can therefore be difficult to represent in national‐scale energy models, and is frequently presumed to be either overly flexible or inflexible. While a few grid models address this complexity via detailed hydraulic process models, more simplified optimization and dispatch models could benefit from the use of empirical parameter values. In this study, we combine a new data set comprising 7.8 million flow‐hours of data from 2011 to 2016 at 158 dams across the United States with monthly and hourly generation data from the U.S. Energy Information Administration (EIA) to elucidate such empirical constraints for the continental United States. We introduce an approach for estimating power generation from hourly water discharge, then present regionally resolved interannual seasonal and diurnal generation patterns; frequency analyses of ramp rates; minimum and maximum generation rates; daily reversals; and load duration curves, all available interactively with a new data visualization tool. We suggest that due largely to hydropower's role as power generation that also serves non‐energy purposes, it acts as a predictable variable generator with constrained dispatchability, more like a supply side analog to demand response resources than like a battery. This observation is particularly relevant for high penetration renewable energy scenarios, given hydroelectric generators' expected value for grid stabilization and load balancing.